Inspired by the recent development of the Transformer network for handling long-range dependencies in sequence transduction tasks, we propose to use a projection-based k-space Transformer network to predict unacquired radial spokes from acquired ones. The Transformer network [1]} has outperformed recurrent and convolutional neural networks in language translation [1]}, image generation [3]} and image classification [4]}. Previous works on vision Transformers proposed to crop the image and reshape the image patches to sequential vectors where each vector is a token. However, cropping and reshaping the image will cut off the global streaking artifact and make the network less effective at removing streaking artifacts.
